http://cvssp-data.eps.surrey.ac.uk/Personal/AdrianHilton/Publications_files/survey_techrep.pdf

A survey of advances in vision-based human motion capture and analysis

A scheme is developed for classifying the types of motion perceived by a humanlike robot. It is assumed that the robot receives visual images of the scene using a perspective system model. Equations, theorems, concepts, clues, etc., relating the objects, their positions, and their motion to their images on the focal plane are presented. >

http://ieeexplore.ieee.org/iel2/815/3148/00100651.pdf

Robot vision

Most automatic expression analysis systems attempt to recognize a small set of prototypic expressions, such as happiness, anger, surprise, and fear. Such prototypic expressions, however, occur rather infrequently. Human emotions and intentions are more often communicated by changes in one or a few discrete facial features. In this paper, we develop an automatic face analysis (AFA) system to analyze facial expressions based on both permanent facial features (brows, eyes, mouth) and transient facial features (deepening of facial furrows) in a nearly frontal-view face image sequence. The AFA system recognizes fine-grained changes in facial expression into action units (AU) of the Facial Action Coding System (FACS), instead of a few prototypic expressions. Multistate face and facial component models are proposed for tracking and modeling the various facial features, including lips, eyes, brows, cheeks, and furrows. During tracking, detailed parametric descriptions of the facial features are extracted. With these parameters as the inputs, a group of action units (neutral expression, six upper face AU and 10 lower face AU) are recognized whether they occur alone or in combinations. The system has achieved average recognition rates of 96.4 percent (95.4 percent if neutral expressions are excluded) for upper face AU and 96.7 percent (95.6 percent with neutral expressions excluded) for lower face AU. The generalizability of the system has been tested by using independent image databases collected and FACS-coded for ground-truth by different research teams.

/pdf/recognizing-action-units-for-facial-expression-analysis-30323i9nup.pdf

Recognizing action units for facial expression analysis

Identification of people by analysis of gait patterns extracted from video has recently become a popular research problem. However, the conditions under which the problem is "solvable" are not understood or characterized. To provide a means for measuring progress and characterizing the properties of gait recognition, we introduce the humanlD gait challenge problem. The challenge problem consists of a baseline algorithm, a set of 12 experiments, and a large data set. The baseline algorithm estimates silhouettes by background subtraction and performs recognition by temporal correlation of silhouettes. The 12 experiments are of increasing difficulty, as measured by the baseline algorithm, and examine the effects of five covariates on performance. The covariates are: change in viewing angle, change in shoe type, change in walking surface, carrying or not carrying a briefcase, and elapsed time between sequences being compared. Identification rates for the 12 experiments range from 78 percent on the easiest experiment to 3 percent on the hardest. All five covariates had statistically significant effects on performance, with walking surface and time difference having the greatest impact. The data set consists of 1,870 sequences from 122 subjects spanning five covariates (1.2 gigabytes of data). This infrastructure supports further development of gait recognition algorithms and additional experiments to understand the strengths and weaknesses of new algorithms. The experimental results are presented, the more detailed is the possible meta-analysis and greater is the understanding. It is this potential from the adoption of this challenge problem that represents a radical departure from traditional computer vision research methodology.

/pdf/the-humanid-gait-challenge-problem-data-sets-performance-and-51qflh0bvn.pdf

The humanID gait challenge problem: data sets, performance, and analysis

Automatic age and gender classification has become relevant to an increasing amount of applications, particularly since the rise of social platforms and social media. Nevertheless, performance of existing methods on real-world images is still significantly lacking, especially when compared to the tremendous leaps in performance recently reported for the related task of face recognition. In this paper we show that by learning representations through the use of deep-convolutional neural networks (CNN), a significant increase in performance can be obtained on these tasks. To this end, we propose a simple convolutional net architecture that can be used even when the amount of learning data is limited. We evaluate our method on the recent Adience benchmark for age and gender estimation and show it to dramatically outperform current state-of-the-art methods.

/pdf/age-and-gender-classification-using-convolutional-neural-4qrrkmmyyh.pdf

Age and gender classification using convolutional neural networks

Age Classification from Facial Images

Flame recognition in video

The invention includes a four step process for automatically finding facial images of a human face in an electronically digitized image (for example, taken by a video-camera), and classifying the age of the person (associated with the face) into an age category. For example three age categories: a baby(up to approximately age 3), a junior person(above age 3 to approximately age forty), and a senior adult (over forty years old). Categories can be further subdivided whereas every three years could be a further age category. Step 1 of the process is to find facial features of the digital image encompassing the chin, sides of the face, virtual top of the head, eyes, mouth and nose of the image. Step 2 is to compute the facial feature ratios of the facial features ratios of the facial features found in Step 1. Step 3 is to compute a wrinkle analysis of the image. Step 4 is to combine the previous two steps to categorize age of the facial image. The invention can locate and detect facial images for age classification from digital camera images and computerized generated images. The invention can be practiced in areas such as population statistic gathering for patrons at entertainment/amusement parks, television viewer ratings. Furthermore, the invention has utility in automated security/surveillance systems, demographic studies, safety monitoring systems, computer human-interface operations and automated photography. The latter to allow for point and shoot focus on specific individuals as a function of their age classification.

Automatic feature detection and age classification of human faces in digital images

Visual methods and systems are described for detecting alertness and vigilance of persons under conditions of fatigue, lack of sleep, and exposure to mind altering substances such as alcohol and drugs. In particular, the intention can have particular applications for truck drivers, bus drivers, train operators, pilots and watercraft controllers and stationary heavy equipment operators, and students and employees during either daytime or nighttime conditions. The invention robustly tracks a person's head and facial features with a single on-board camera with a fully automatic system, that can initialize automatically, and can reinitialize when it need's to and provide outputs in realtime. The system can classify rotation in all viewing direction, detects' eye/mouth occlusion, detects' eye blinking, and recovers the 3D(three dimensional) gaze of the eyes. In addition, the system is able to track both through occlusion like eye blinking and also through occlusion like rotation. Outputs can be visual and sound alarms to the driver directly. Additional outputs can slow down the vehicle cause and/or cause the vehicle to come to a full stop. Further outputs can send data on driver, operator, student and employee vigilance to remote locales as needed for alarms and initiating other actions.

/pdf/algorithm-for-monitoring-head-eye-motion-for-driver-4f4hz9tfnd.pdf

Algorithm for monitoring head/eye motion for driver alertness with one camera

This is a method for physically-based modeling of three dimensional (3D) general fluid animation in computer graphics. By solving the two dimensional (2D) Navier-Stokes equations using a Computational Fluid Dynamics method, we map it into 3D using the corresponding pressures in the fluid flow field. The method achieves realistic real-time fluid animation by solving the physical governing laws of fluids but avoiding the extensive 3D fluid dynamics computation. Unlike previous computer graphics fluid models, our approach can model many different fluid behaviors by changing the internal or external boundary conditions. It can also model different kinds of fluids by varying the Reynolds number. In addition, we have generated several ways to view the animation of fluid flow field, the streakline of the flow field and the blending of fluids of different colors. Our model can serve as a testbed to simulate many more fluid phenomena which have never been successfully modeled.

/pdf/3d-realtime-fluid-animation-by-navier-stokes-equations-32aedd74t6.pdf

Niels da Vitoria Lobo

Papers

Age Classification from Facial Images

Flame recognition in video

Automatic feature detection and age classification of human faces in digital images

Algorithm for monitoring head/eye motion for driver alertness with one camera

3D realtime fluid animation by Navier-Stokes equations